SL VI Mini Project

Stock Market Analysis

Group Id 3

Importing Libraries

import numpy as np
import pandas as pd
import datetime as dt #for getting date
from nsepy import get_history as gh # for importing nse datasets
import dateutil.relativedelta as dr # for date operation
%matplotlib inline
import matplotlib.pyplot as plt #for visualization
import seaborn as sns
import plotly.graph_objects as go
import plotly.express as px
plt.style.use('seaborn-darkgrid')
plt.rc('figure', figsize=(14,8))
plt.rc('lines', markersize=4)
import warnings
warnings.filterwarnings("ignore")

Loading Data using NSEPy Library

end=dt.date.today()
start=end-dr.relativedelta(days=360)

stk_data = gh(symbol='SBIN',start=start,end=end)

data=stk_data

Analyzing Dataset

#displaying top 5 entries
data.head()

	Symbol	Series	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
Date
2020-06-16	SBIN	EQ	173.70	178.0	178.95	169.40	172.55	172.90	174.16	63929925	1.113374e+15	295248	9484014	0.1484
2020-06-17	SBIN	EQ	172.90	171.0	176.20	169.25	172.80	172.95	173.19	73867145	1.279334e+15	337640	10915007	0.1478
2020-06-18	SBIN	EQ	172.95	172.4	180.65	171.10	180.00	179.65	176.37	58262367	1.027583e+15	274070	12595472	0.2162
2020-06-19	SBIN	EQ	179.65	180.3	184.95	179.50	184.25	184.50	182.93	76897890	1.406723e+15	324544	15049467	0.1957
2020-06-22	SBIN	EQ	184.50	185.0	191.75	184.25	187.90	187.70	188.68	80024673	1.509941e+15	412445	16662588	0.2082

data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 249 entries, 2020-06-16 to 2021-06-11
Data columns (total 14 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Symbol              249 non-null    object 
 1   Series              249 non-null    object 
 2   Prev Close          249 non-null    float64
 3   Open                249 non-null    float64
 4   High                249 non-null    float64
 5   Low                 249 non-null    float64
 6   Last                249 non-null    float64
 7   Close               249 non-null    float64
 8   VWAP                249 non-null    float64
 9   Volume              249 non-null    int64  
 10  Turnover            249 non-null    float64
 11  Trades              249 non-null    int64  
 12  Deliverable Volume  249 non-null    int64  
 13  %Deliverble         249 non-null    float64
dtypes: float64(9), int64(3), object(2)
memory usage: 29.2+ KB

#showing columns
data.columns

Index(['Symbol', 'Series', 'Prev Close', 'Open', 'High', 'Low', 'Last',
       'Close', 'VWAP', 'Volume', 'Turnover', 'Trades', 'Deliverable Volume',
       '%Deliverble'],
      dtype='object')

data.describe()

	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
count	249.000000	249.000000	249.000000	249.000000	249.000000	249.000000	249.000000	2.490000e+02	2.490000e+02	2.490000e+02	2.490000e+02	249.000000
mean	276.348394	277.364458	281.836345	272.774900	277.517068	277.376104	277.410602	5.601436e+07	1.533005e+15	3.090954e+05	1.203947e+07	0.215116
std	84.072545	84.466617	85.667620	83.153914	84.486972	84.376226	84.401883	2.759489e+07	9.476667e+14	1.416496e+05	6.923657e+06	0.058696
min	172.900000	171.000000	176.200000	169.250000	172.550000	172.900000	173.190000	7.753219e+06	1.790815e+14	7.352100e+04	2.345791e+06	0.090000
25%	195.450000	196.000000	199.100000	192.500000	195.700000	195.600000	195.680000	3.914786e+07	9.550262e+14	2.208730e+05	7.523211e+06	0.170300
50%	263.000000	263.800000	268.850000	255.600000	264.050000	263.500000	261.140000	4.917870e+07	1.325488e+15	2.777390e+05	1.049837e+07	0.213000
75%	358.250000	360.000000	364.300000	354.550000	358.200000	358.550000	358.650000	6.490837e+07	1.761546e+15	3.400870e+05	1.457824e+07	0.257500
max	439.650000	437.500000	441.950000	433.500000	439.050000	439.650000	437.300000	2.149557e+08	8.549083e+15	1.155236e+06	4.310213e+07	0.419300

#checking for Null columns
data.dropna(inplace=True)
data.isnull().sum()

Symbol                0
Series                0
Prev Close            0
Open                  0
High                  0
Low                   0
Last                  0
Close                 0
VWAP                  0
Volume                0
Turnover              0
Trades                0
Deliverable Volume    0
%Deliverble           0
dtype: int64

df=pd.DataFrame(data, columns=['Close','Prev Close', 'Open', 'High', 'Low', 'Last',
       'VWAP', 'Volume', 'Turnover', 'Trades', 'Deliverable Volume',
       '%Deliverble'])
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Fit on training set only.
scaler.fit(df)
df

	Close	Prev Close	Open	High	Low	Last	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
Date
2020-06-16	172.90	173.70	178.00	178.95	169.40	172.55	174.16	63929925	1.113374e+15	295248	9484014	0.1484
2020-06-17	172.95	172.90	171.00	176.20	169.25	172.80	173.19	73867145	1.279334e+15	337640	10915007	0.1478
2020-06-18	179.65	172.95	172.40	180.65	171.10	180.00	176.37	58262367	1.027583e+15	274070	12595472	0.2162
2020-06-19	184.50	179.65	180.30	184.95	179.50	184.25	182.93	76897890	1.406723e+15	324544	15049467	0.1957
2020-06-22	187.70	184.50	185.00	191.75	184.25	187.90	188.68	80024673	1.509941e+15	412445	16662588	0.2082
...	...	...	...	...	...	...	...	...	...	...	...	...
2021-06-07	432.25	433.60	435.10	437.40	430.80	431.30	434.78	20201197	8.783035e+14	174837	6487715	0.3212
2021-06-08	427.20	432.25	432.25	432.50	424.00	427.40	427.47	31113823	1.330030e+15	247824	9816376	0.3155
2021-06-09	421.45	427.20	427.50	432.30	418.20	421.95	426.58	27784834	1.185249e+15	241080	6008214	0.2162
2021-06-10	432.25	421.45	424.90	433.30	422.40	432.40	428.67	27397712	1.174471e+15	211924	7523211	0.2746
2021-06-11	429.60	432.25	434.85	435.50	425.25	429.40	430.04	26831924	1.153870e+15	211915	6400469	0.2385

249 rows × 12 columns

Visualization using matplolib

#plotting close values
data[["Close"]].plot()

<AxesSubplot:xlabel='Date'>

#plotting density of volume traded
data[["Volume"]].plot.density()

<AxesSubplot:ylabel='Density'>

#selecting columns as per requirement
df = pd.DataFrame(data,columns = ['Open', 'Close'])

df.plot()
# Show the legend
plt.legend()
# Define the label for the title of the figure
plt.title("OPEN VS CLOSE PRICES", fontsize=16)
# Define the labels for x-axis and y-axis
plt.ylabel('Price', fontsize=14)
plt.xlabel('Year', fontsize=14)
# Plot the grid lines
plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5)
plt.show()

#selecting columns as per requirement
df = pd.DataFrame(data,columns = ['High', 'Low'])
df.plot()
# Show the legend
plt.legend()
# Define the label for the title of the figure
plt.title("High VS Low PRICES", fontsize=16)
# Define the labels for x-axis and y-axis
plt.ylabel('Price', fontsize=14)
plt.xlabel('Year', fontsize=14)
# Plot the grid lines
plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5)
plt.show()

Visualization using interactive graphs with plotly

stk_data['Date'] = stk_data.index
data2 = pd.DataFrame(columns = ['Date', 'Open', 'High', 'Low', 'Close'])
data2['Date'] = stk_data['Date']
data2['Open'] = stk_data['Open']
data2['High'] = stk_data['High']
data2['Low'] = stk_data['Low']
data2['Close'] = stk_data['Close']

fig = go.Figure(data=[go.Candlestick(x=data2['Date'],
                open=data2['Open'],
                high=data2['High'],
                low=data2['Low'],
                close=data2['Close'])])

fig.update_layout(
    title='SBIN',
    yaxis_title='Price in RS.',
    
)
fig.show()

Calculation to find the percent change for each day of stock

data2['Daily Return'] = data2['Close'].pct_change()

# Visualization of the percent change for each day of stock
data2['Daily Return'].plot(figsize=(12,4),legend=True,linestyle='--',marker='o')

<AxesSubplot:xlabel='Date'>

# Histogram to visualiza the average daily return of Apple stock
sns.distplot(data2['Daily Return'].dropna(),bins=100,color='purple')

<AxesSubplot:xlabel='Daily Return', ylabel='Density'>

Visulaztion of Support And Resistance of Stock

The Resistance

As the name suggests, resistance is something which stops the price from rising further. The resistance level is a price point on the chart where traders expect maximum supply (in terms of selling) for the stock/index. The resistance level is always above the current market price.

The support

The support level is a price point on the chart where the trader expects maximum demand (in terms of buying) coming into the stock/index. Whenever the price falls to the support line, it is likely to bounce back. The support level is always below the current market price.

#importing libraries for calculating resistance
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mpl_dates
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [12, 7]
plt.rc('font', size=14)

C:\Users\DELL\anaconda3\lib\site-packages\mpl_finance.py:16: DeprecationWarning:



  =================================================================

   WARNING: `mpl_finance` is deprecated:

    Please use `mplfinance` instead (no hyphen, no underscore).

    To install: `pip install --upgrade mplfinance` 

   For more information, see: https://pypi.org/project/mplfinance/

  =================================================================

df=pd.DataFrame(data, columns=['Open', 'High', 'Low', 'Close'])

df['Date'] = pd.to_datetime(df.index)
df['Date'] = df['Date'].apply(mpl_dates.date2num)
df = df.loc[:,['Date', 'Open', 'High', 'Low', 'Close']]

def isSupport(df,i):
  support = df['Low'][i] < df['Low'][i-1]  and df['Low'][i] < df['Low'][i+1] and df['Low'][i+1] < df['Low'][i+2] and df['Low'][i-1] < df['Low'][i-2]
  return support

def isResistance(df,i):
  resistance = df['High'][i] > df['High'][i-1]  and df['High'][i] > df['High'][i+1] and df['High'][i+1] > df['High'][i+2] and df['High'][i-1] > df['High'][i-2]
  return resistance

levels = []
for i in range(2,df.shape[0]-2):
  if isSupport(df,i):
    levels.append((i,df['Low'][i]))
  elif isResistance(df,i):
    levels.append((i,df['High'][i]))

def plot_all():
  fig, ax = plt.subplots()
  candlestick_ohlc(ax,df.values,width=0.6, \
                   colorup='green', colordown='red', alpha=0.8)
  date_format = mpl_dates.DateFormatter('%d %b %Y')
  ax.xaxis.set_major_formatter(date_format)
  fig.autofmt_xdate()
  fig.tight_layout()
  for level in levels:
    plt.hlines(level[1],xmin=df['Date'][level[0]],\
               xmax=max(df['Date']),colors='blue')
  fig.show()

plot_all()

s =  np.mean(df['High'] - df['Low'])
def isFarFromLevel(l):
   return np.sum([abs(l-x) < s  for x in levels]) == 0
levels = []
for i in range(2,df.shape[0]-2):
  if isSupport(df,i):
    l = df['Low'][i]
    if isFarFromLevel(l):
      levels.append((i,l))
  elif isResistance(df,i):
    l = df['High'][i]
    if isFarFromLevel(l):
      levels.append((i,l))

plot_all()

Comparing Banks stocks using Percentage Returns

#importing required data
sbi = gh(symbol='SBIN',start=start,end=end)
hdfc = gh(symbol='HDFCBANK',start=start,end=end)
axis = gh(symbol='AXISBANK',start=start,end=end)
icici = gh(symbol='ICICIBANK',start=start,end=end)

#creating required dataframe
banks_close=pd.DataFrame(columns = ['sbi_close', 'hdfc_close', 'axis_close', 'icici_close'])
banks_close['sbi_close'] = sbi['Close']
banks_close['hdfc_close'] = hdfc['Close']
banks_close['axis_close'] = axis['Close']
banks_close['icici_close'] = icici['Close']

banks_close.head()

	sbi_close	hdfc_close	axis_close	icici_close
Date
2020-06-16	172.90	990.40	381.55	342.95
2020-06-17	172.95	979.25	389.60	341.95
2020-06-18	179.65	1019.95	405.40	352.00
2020-06-19	184.50	1033.35	417.05	363.80
2020-06-22	187.70	1028.75	430.15	367.55

# Calculate the daily return percent of all stocks and store them in a new banks returns DataFrame
bank_rets = banks_close.pct_change()

Visualization of compared stocks using Seaborn

# Comparing Google to itself shows a perfectly linear relationship
sns.jointplot('sbi_close','sbi_close',bank_rets,kind='scatter',color='seagreen')

<seaborn.axisgrid.JointGrid at 0x1cb8e0baf10>

So now we know that if two stocks are perfectly (and positivley) correlated with each other a linear relationship bewteen its daily return values should occur. So let's go ahead and compare stocks with each other

#Joinplot to compare the daily returns of sbi and  hdfc
sns.jointplot('sbi_close','hdfc_close',bank_rets,kind='scatter',color='seagreen')

<seaborn.axisgrid.JointGrid at 0x1cb8d555a60>

# Correlation analysis for every possible combination of stocks in our bank stock ticker list.
sns.pairplot(bank_rets.dropna())

<seaborn.axisgrid.PairGrid at 0x1cb8d55be80>

#correlation heatmap of bank stocks
sns.heatmap(bank_rets.corr(), annot=True, annot_kws={"size":15})

<AxesSubplot:>

# Cleaning data frame by droping rows having null values
rets = bank_rets.dropna()

# Scatter plot of expected return of the stocks vs. their standard deviations of daily retunrs
area = np.pi*20

plt.scatter(rets.mean(), rets.std(),alpha = 0.5,s =area)

plt.ylim([0.01,0.025])
plt.xlim([-0.003,0.004])

plt.xlabel('Expected returns')
plt.ylabel('Risk')

for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 30),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=0.5'))

It can be visulaized from the above plot that AXIS bank stock have higher risk but offers higher returns than other stocks whereas HDFC stock have lower risk but offers less return on its stock.

Correlational Analysis of SBI stock

data.corr()

	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
Prev Close	1.000000	0.999191	0.997767	0.998043	0.996411	0.996555	0.997856	-0.118476	0.397545	0.294217	0.034116	0.334819
Open	0.999191	1.000000	0.998804	0.998948	0.997280	0.997404	0.998844	-0.103748	0.413969	0.307537	0.045852	0.332442
High	0.997767	0.998804	1.000000	0.998955	0.999140	0.999196	0.999700	-0.078245	0.437601	0.329604	0.068772	0.333786
Low	0.998043	0.998948	0.998955	1.000000	0.998743	0.998899	0.999664	-0.105541	0.412210	0.300903	0.044645	0.332957
Last	0.996411	0.997280	0.999140	0.998743	1.000000	0.999962	0.999339	-0.082380	0.433738	0.322913	0.065903	0.334357
Close	0.996555	0.997404	0.999196	0.998899	0.999962	1.000000	0.999445	-0.084056	0.431994	0.320850	0.064365	0.334026
VWAP	0.997856	0.998844	0.999700	0.999664	0.999339	0.999445	1.000000	-0.090068	0.426468	0.316264	0.057945	0.332941
Volume	-0.118476	-0.103748	-0.078245	-0.105541	-0.082380	-0.084056	-0.090068	1.000000	0.826138	0.868815	0.853349	0.011320
Turnover	0.397545	0.413969	0.437601	0.412210	0.433738	0.431994	0.426468	0.826138	1.000000	0.948940	0.762406	0.146702
Trades	0.294217	0.307537	0.329604	0.300903	0.322913	0.320850	0.316264	0.868815	0.948940	1.000000	0.806694	0.163596
Deliverable Volume	0.034116	0.045852	0.068772	0.044645	0.065903	0.064365	0.057945	0.853349	0.762406	0.806694	1.000000	0.476871
%Deliverble	0.334819	0.332442	0.333786	0.332957	0.334357	0.334026	0.332941	0.011320	0.146702	0.163596	0.476871	1.000000

corMat = (data.iloc[:,:14].corr()).values
corMat = np.around(corMat, decimals = 3)
print(corMat)

[[ 1.     0.999  0.998  0.998  0.996  0.997  0.998 -0.118  0.398  0.294
   0.034  0.335]
 [ 0.999  1.     0.999  0.999  0.997  0.997  0.999 -0.104  0.414  0.308
   0.046  0.332]
 [ 0.998  0.999  1.     0.999  0.999  0.999  1.    -0.078  0.438  0.33
   0.069  0.334]
 [ 0.998  0.999  0.999  1.     0.999  0.999  1.    -0.106  0.412  0.301
   0.045  0.333]
 [ 0.996  0.997  0.999  0.999  1.     1.     0.999 -0.082  0.434  0.323
   0.066  0.334]
 [ 0.997  0.997  0.999  0.999  1.     1.     0.999 -0.084  0.432  0.321
   0.064  0.334]
 [ 0.998  0.999  1.     1.     0.999  0.999  1.    -0.09   0.426  0.316
   0.058  0.333]
 [-0.118 -0.104 -0.078 -0.106 -0.082 -0.084 -0.09   1.     0.826  0.869
   0.853  0.011]
 [ 0.398  0.414  0.438  0.412  0.434  0.432  0.426  0.826  1.     0.949
   0.762  0.147]
 [ 0.294  0.308  0.33   0.301  0.323  0.321  0.316  0.869  0.949  1.
   0.807  0.164]
 [ 0.034  0.046  0.069  0.045  0.066  0.064  0.058  0.853  0.762  0.807
   1.     0.477]
 [ 0.335  0.332  0.334  0.333  0.334  0.334  0.333  0.011  0.147  0.164
   0.477  1.   ]]

feature_importance = (data.iloc[:,:14].corr()).iloc[:-1, -1].sort_values(ascending=False)
print('Features in Descending Order of Importance', list(feature_importance.index))

Features in Descending Order of Importance ['Deliverable Volume', 'Prev Close', 'Last', 'Close', 'High', 'Low', 'VWAP', 'Open', 'Trades', 'Turnover', 'Volume']

data.head()

	Symbol	Series	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble	Date
Date
2020-06-16	SBIN	EQ	173.70	178.0	178.95	169.40	172.55	172.90	174.16	63929925	1.113374e+15	295248	9484014	0.1484	2020-06-16
2020-06-17	SBIN	EQ	172.90	171.0	176.20	169.25	172.80	172.95	173.19	73867145	1.279334e+15	337640	10915007	0.1478	2020-06-17
2020-06-18	SBIN	EQ	172.95	172.4	180.65	171.10	180.00	179.65	176.37	58262367	1.027583e+15	274070	12595472	0.2162	2020-06-18
2020-06-19	SBIN	EQ	179.65	180.3	184.95	179.50	184.25	184.50	182.93	76897890	1.406723e+15	324544	15049467	0.1957	2020-06-19
2020-06-22	SBIN	EQ	184.50	185.0	191.75	184.25	187.90	187.70	188.68	80024673	1.509941e+15	412445	16662588	0.2082	2020-06-22

Preparing and showing data for predictions

#selecting columns which are not highly correalted
df = pd.DataFrame(data, columns=['Deliverable Volume', 'Prev Close', 'Last', 'Close', 'High', 'Low', 'VWAP'])


# Reset index column so that we have integers to represent time for later analysis
df = df.reset_index()
import matplotlib.dates as mdates

years = mdates.YearLocator()
yearsFmt = mdates.DateFormatter('%Y') 


fig, ax = plt.subplots()
ax.plot(df['Date'], df['Close'])


ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)


plt.title('Close Stock Price History [2020 - 2021]', fontsize=16)

plt.xlabel('Date', fontsize=14)

plt.ylabel('Closing Stock Price in Rs.', fontsize=14)

fig.autofmt_xdate()


plt.show()

#spliting data into train and test split
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.20)

X_train = np.array(train.index).reshape(-1, 1) # reshaping into 2-d array for prediction
y_train = train['Close']

Building Linear Regression Model for Prediction

The term “linearity” in algebra refers to a linear relationship between two or more variables. If we draw this relationship in a two-dimensional space (between two variables), we get a straight line. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x).

#importing liabraries and loading model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

LinearRegression()

#comparing predicted  values with original close 
plt.figure(1, figsize=(16,10))
plt.title('Linear Regression | Price vs Time')
plt.scatter(X_train, y_train, edgecolor='w', label='Actual Price')
plt.plot(X_train, model.predict(X_train), color='r', label='Predicted Price')
plt.xlabel('Integer Date')
plt.ylabel('Stock Price')
plt.legend()
plt.show()

X_test = np.array(test.index).reshape(-1, 1)
y_test = test['Close']

y_pred = model.predict(X_test)

# The coefficient
print('Slope: ', np.asscalar(np.squeeze(model.coef_)))
# The Intercept
print('Intercept: ', model.intercept_)

Slope:  1.0925854771255972
Intercept:  140.17029910804084

df_pred=pd.DataFrame(data, columns=['Close'])

#adding linear prediction column in dataset
df_pred['Prediction_LR'] = model.predict(np.array(df.index).reshape(-1, 1))

print("Test Accuracy:",model.score(X_test, y_test))

Test Accuracy: 0.8436939431631799

Calculating RMSE MAE RMSE for evaluation of model

from sklearn import metrics

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Mean Absolute Error: 26.948458054455674
Mean Squared Error: 1123.9344266996968
Root Mean Squared Error: 33.52513127043199

df_pred.plot()
# Show the legend
plt.legend()
# Define the label for the title of the figure
plt.title("Prediction VS CLOSE PRICES", fontsize=16)
# Define the labels for x-axis and y-axis
plt.ylabel('Price', fontsize=14)
plt.xlabel('Integer Date', fontsize=14)
# Plot the grid lines
plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5)
plt.show()

Randomforest Regressor

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

#importing liabraries and loading model
from sklearn.ensemble import RandomForestRegressor

reg = RandomForestRegressor(n_estimators=100)
reg.fit(X_train,y_train)

RandomForestRegressor()

X_test = np.array(test.index).reshape(-1, 1)
y_test = test['Close']

y_pred = reg.predict(X_test)

#adding Randomforest prediction column in dataset
df_pred['Prediction_RF'] = reg.predict(np.array(df.index).reshape(-1, 1))

df.head()

	Date	Deliverable Volume	Prev Close	Last	Close	High	Low	VWAP
0	2020-06-16	9484014	173.70	172.55	172.90	178.95	169.40	174.16
1	2020-06-17	10915007	172.90	172.80	172.95	176.20	169.25	173.19
2	2020-06-18	12595472	172.95	180.00	179.65	180.65	171.10	176.37
3	2020-06-19	15049467	179.65	184.25	184.50	184.95	179.50	182.93
4	2020-06-22	16662588	184.50	187.90	187.70	191.75	184.25	188.68

#accuracy of random forest model
print("Test Accuracy:",reg.score(X_test, y_test))

Test Accuracy: 0.9937242475503509

Calculating RMSE MAE RMSE for evaluation of model

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Mean Absolute Error: 4.6773500000000805
Mean Squared Error: 45.12642935500071
Root Mean Squared Error: 6.717620810599591

Comparing RandomForest with linear Regressor

df_pred.plot()
# Show the legend
plt.legend()
# Define the label for the title of the figure
plt.title("Random Forest vs Linear", fontsize=16)
# Define the labels for x-axis and y-axis
plt.ylabel('Price', fontsize=14)
plt.xlabel('Integer Date', fontsize=14)
# Plot the grid lines
plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5)
plt.show()

Support Vector Regressor

Support Vector Machine (SVM) is a relatively simple Supervised Machine Learning Algorithm used for classification and/or regression. It is more preferred for classification but is sometimes very useful for regression as well. Basically, SVM finds a hyper-plane that creates a boundary between the types of data. In 2-dimensional space, this hyper-plane is nothing but a line.

from sklearn import svm

clf = svm.SVR()
clf.fit(X_train,y_train)

SVR()

X_test = np.array(test.index).reshape(-1, 1)
y_test = test['Close']

y_pred = clf.predict(X_test)

df_pred['Prediction_SVM'] = clf.predict(np.array(df.index).reshape(-1, 1))

Calculating RMSE MAE RMSE for evaluation of model

#accuracy of SVm model
print("Test Accuracy:",clf.score(X_test, y_test))

Test Accuracy: 0.71573329718768

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Mean Absolute Error: 31.063740508017347
Mean Squared Error: 2044.048324939357
Root Mean Squared Error: 45.21115266103439

Comparing all Machine Learning Models

df_pred.plot()
# Show the legend
plt.legend()
# Define the label for the title of the figure
plt.title("Random Forest vs Linear", fontsize=16)
# Define the labels for x-axis and y-axis
plt.ylabel('Price', fontsize=14)
plt.xlabel('Integer Date', fontsize=14)
# Plot the grid lines
plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5)
plt.show()